A Statistical Filtering Algorithm for Tender Price Evaluation Using the J-Selaras Framework

Accurate evaluation of tender prices is critical in construction procurement, particularly in public sector projects where financial efficiency and transparency are essential. However, traditional evaluation methods are often affected by abnormal bids, leading to inconsistent and potentially biased decision-making. This study proposes the J-Selaras Statistical Filtering Algorithm (JSFA), a data-driven approach that integrates normalization relative to Anggaran Jabatan (AJ), Z-score-based outlier detection, and a dual-threshold mechanism for cut-off determination. A simulated tender dataset was constructed to represent realistic bidding behavior, including both typical and extreme pricing scenarios. The results demonstrate that the proposed algorithm effectively identifies abnormal bids, reduces data dispersion, and stabilizes the estimation of central tendency. The initial dataset exhibited high variability (σ=44.63), which was significantly improved after filtering, resulting in an adjusted mean of 〖μ〗_adj=10.83. The final cut-off value (C=9.21) provides a statistically grounded threshold for tender evaluation. The findings indicate that JSFA enhances the objectivity, consistency, and transparency of procurement decision-making by mitigating the influence of outliers. The algorithm is computationally efficient and scalable, making it suitable for integration into digital procurement systems such as J-Selaras. This study contributes a practical and reproducible framework for improving tender evaluation processes in public sector construction projects.

@article{Man2026,
  title     = {A Statistical Filtering Algorithm for Tender Price Evaluation Using the J-Selaras Framework},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {7},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mustafa Man and Wan Aezwani Wan Abu Bakar and Mohd Kamir Yusof and Mohd Nasir Saari and Nor Arzalifah Abd. Razak and Nor Zuraiya Mt Amin},
  doi       = {10.14569/IJACSA.2026.0170740},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170740}
}